Category Archives: Information Science

Information science, discipline that deals with the processes of storing and transferring information. It attempts to bring together concepts and methods from various disciplines such as library science, computer science and engineering, linguistics, psychology, and other technologies in order to develop techniques and devices to aid in the handling—that is, in the collection, organization, storage, retrieval, interpretation, and use—of information.

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Cloud computing is a service driven model for enabling ubiquitous, convenient, on demand network access to a shared pool computing resources that can be rapidly provisioned and released with minimal administrative effort or service provider interaction.

Today, newfound efficiencies and innovation are key to any business success – small, medium or large. In the rapidly evolving field of data analytics, innovative approaches to handling data are particularly important since data is the most valuable resource any business can have. IBM common SQL Engine is delivering application and query compatibility that is allowing companies to turn their data into actionable insights. This is allowing businesses to unleash the power of their databases without constraints.

But, is this really important?

Yes. Many businesses have accumulated tons of data over the years. This data resides in higher volumes, more locations throughout an enterprise – on-premise and on-cloud –, and in greater variety. Typically, this data should be a huge advantage, providing enterprises with actionable insights. But, often, this doesn’t happen.

IBM Hybrid Data Management.

With such a massive barrel of complex legacy data, many organizations find it confusing to decide what to do with it. Or where to start. The process of migrating all that data into new systems is simply a non-starter. As a solution, enterprises are turning to IBM Db2 – a hybrid, intuitive data approach that marries data and analytics seamlessly. IBM Db2 hybrid data management allows flexible cloud and on-premises deployment of data.

However, such levels of flexibility typically require organizations to rewrite or restructure their queries, and applications that will use the diverse, ever-changing data. These changes may even require you to license new software. This is costly and unfeasible. To bridge this gap, the Common SQL Engine (CSE) comes into play.

How IBM Common SQL Engine is Positioning Db2 for the Future?

The IBM Common SQL Engine inserts a single layer of data abstraction at the very data source. This means that, instead of migrating the data all at once, you can now apply data analytics wherever the data resides – whether on private, public or hybrid cloud – by using the Common SQL Engine as a bridge.

The IBM’s Common SQL Engine provides portability and consistency of SQL commands, meaning that the SQL is functionally portable across multiple implementations. It allows seamless movement of workloads to the cloud and allows for multiplatform integration and configurations regardless of their programming language.

Ideally, the Common SQL Engine is supposed to be the heart of the query and the foundation of application compatibility. But it does so much more!

Its compatibility extends beyond data analytic applications to include security, management, governance, data management, and other functionalities as well.

How does this improve the quality, flexibility, and portability of Db2?

By allowing for integration across multiple platforms, workloads and programming languages, the Common SQL Engine, ultimately, leads to a “data without limits” environment for Db2 hybrid data management family through:

Query and application compatibility

The Common SQL engine (CSE) ensures that users can write a query, and be confident that it will work across the Db2 hybrid data management family of offerings. With the CSE, you can change your data infrastructure and location – on-cloud or on-premises – without having to worry about license costs and application compatibility.

Data virtualization and Integration

The common SQL engine has a built-in data virtualization service that ensures that you can access your data from all your sources. These services position Db2 family of offerings including, IBM Db2 warehouse, IBM Db2, IBM Db2 BigSQL amongst others.

This services also applies to IBM Integrated Analytics System, Teradata, Oracle, Puredata and Microsoft SQL server. Besides, you can work seamlessly with open-source solutions such as HIVE; and cloud sources such as Amazon Redshift. Such levels of integration are unprecedented!

By allowing users to effectively pull data from Db2 data stores and integrate it with data from non-IBM stores using a single query, the common SQL engine places Db2 at an authoritative position as compared to other data stores.

Flexible Licensing

Licensing is one of the hardest nuts to crack, especially for smart organizations who rely on technologies such as the cloud to deliver their services. While application compatibility and data integration will save you time, flexible licensing saves you money, on the spot.

IBM’s common SQL engine allows flexible licensing, meaning that you can purchase one license model and deploy it whenever needed, or as your data architecture evolves. Using IBM’s FlexPoint licensing, you can purchase FlexPoints and use them across all Db2 data management offerings. This is a convenience in one place.

The flexible licensing will not only simplify the adoption and exchange of platform capabilities, but it also positions your business strategically by making it more agile. Your data managers will be able to access the tools needed on the fly, without going through a lethargic and tedious procurement process.

IBM Db2 Data Management Family Is Supported by Common SQL Engine (CSE) .

IBM Db2 is a family of custom, deployable database that allows enterprises to leverage existing investments. IBM Db2 allows businesses to use any type of data from an either structured or unstructured database (or data warehouse). It provides the right data foundation/environment with industry-leading data compression, on-premise and cloud deployment options, modern data security, robust performance for mixed loads and the ability to adjust and scale without redesigning.

The IBM Db2 family enable businesses to adapt, scale quickly and remain competitive without compromising security, risk levels or privacy. It features:

Embedded IoT technology is allowing businesses to act fast on the fly.

Some of these Db2 family offerings that are supported by the common SQL engine include:

Db2 Database

Db2 Hosted

Db2 Big SQL

Db2 on Cloud

Db2 Warehouse

Db2 Warehouse on Cloud

IBM Integrated Analytics System (IIAS)

Db2 Family Offerings and Beyond

Since the common SQL engine mainly focuses on data federation and propensity, other non-IBM databases can as well plug into the engine for SQL processing. These other 3rd party offerings include:

Watson Data Platform

Oracle

Hadoop

Microsoft SQL Server

Teradata

Hive

Conclusion

IBM Common SQL engine is allowing organizations to fully use data analytics to future-proof their business, and as well remain agile and competitive. In fact, besides the benefits of having robust tools woven into CSE, this SQL engine offers superior analytics and machine-learning positioning. Data processing can now happen at the speed of light –- 2X to 5X faster. The IBM Common SQL engine adds important capabilities to Db2, including freedom of location, freedom of use, and freedom of assembly.

Data analytics has changed where data is no longer manageable in relational databases only. Data is flowing from various sources which are not of the same format. This means it is not possible to store all data in the same repository. Some are best suited for storing in relational databases, others for Apache Hadoop while others are best suited for NoSQL databases.

During data analyzing, so much time is taken in trying to bring the distributed data together instead of obtaining insights. Db2 Federation has come to the rescue of data analysts. Federation concept in db2 eliminates the need for storing data in different repositories and reduces the hassle of getting insights.

What is DB2 Federation?

DB2 federation is a data integration technology that permits remote database objects to be accessed as local DB2 database objects. This technology connects multiple databases and makes them appear like one database.

How does DB2 federation work?

Federation allows you to access all of your data that is on multiple distributed databases using a single query. When implemented in an organization, this technology can be used to access data that is on any of the organization’s Db2, whether local or in the cloud.

Why use DB2 federation?

So, why should you use the federation? This concept brings data of all formats into one virtual source. With data being retrieved from one virtual source, analyzing it becomes cost-effective and efficient.

What are its primary use cases for DB2 federation?

Merging of various sources of data

DB2 federation facilitates consolidating of data from sources data local and cloud to form one virtual data source. This eliminates the process of migrating data which can be expensive and troublesome.

Increase the capacity of a repository beyond the fixed limits

Physical storage capacity is bound to have a limit which is one reason you may find an organization has distributed its data in various repositories. With federation, the storage is virtual and therefore doesn’t have any limit. This technology can greatly help you if your physical dataset is running low on space.

Linking up to Db2 Warehouse on Cloud

People who use Db2 products can federate data from Db2 on Cloud and Db2 Warehouse on the Cloud. This will give them a joint interface where they can access, add, query, and analyze data without encountering the complex ETL processes. Better still, no additional code will be required to execute all these processes. This makes it easy for people with the low technical know-how to use these products smoothly.

Split data across different servers

At times, you might choose to partition your data. With federation integration technology, partitioned data can be queried with a unified interface. Federation allows you to better balance your workloads, scale precise parts of an app, and create micro-services that work harmoniously.

Generally, db2 federation makes it access data by bringing it together into a single virtual source. This brings about cost and time-saving benefits. When you want to analyze data, you can get insights immediately instead of spending a lot of time querying through repositories.

A Road Map for Migrating to A Public Cloud Environment

Today, most organizations are looking for ways to cut down their sprawling IT budgets and define efficient paths for new developments. Making the move to the cloud is being seen as a more strategic and an economically viable idea, that is primarily allowing organizations to gain quick access to new platforms, services, and toolsets. But the migration of applications to the cloud environment needs a clear, and well-thought-out cloud migration strategy.

We are past the year of confusion and fear on matters cloud environment. In fact, almost everyone now agrees that the cloud is a key element of any company’s IT investment. What is not yet clear is what to move, how to move, and industry best practices to protect your investment in a public cloud environment. Therefore, a solid migration plan is an essential part of any cloud migration process.

Here are a few things you should pay close attention to when preparing a cloud migration planning template:

Data Protection

When planning to migrate to the cloud, it is paramount to remember that it is not a good idea to migrate every application. As you learn the baby steps, keep your legacy apps and other sensitive data such as private banking info off the cloud. This will ensure that, in case of a breach on your public cloud, your sensitive data and legacy systems will not fall into the hands of unsavory individuals.

Security

Security of the data being migrated to the cloud should be just as important as on the cloud. Any temporary storage locations used during the cloud migration process should be secure from unauthorized intrusions.

Although security can be hard to quantify, it is one of the key components and considerations of any cloud service. The very basic security responsibility includes getting it right around your password security. Remember that, while you can massively increase the security around your applications, it is practically very different to deal with on-cloud threats and breaches since you technically don’t own any of the cloud software.

Some of the security concerns that you’ll need to look into include:

Is your data securely transferred and stored in the cloud?

Besides the passwords, does your cloud provider offer some type of 2-factor authentication?

How else are the users authenticated?

Does your provider meet the industry’s regulatory requirements?

Backup and Disaster recovery strategies

A backup and disaster strategy ensure that your data will be protected in case of a disaster. These strategies are unique to every organization depending on its application needs and the relevance of those applications to their organization.

To devise a foolproof DR strategy, it is important to identify and prioritize applications and determine the downtime acceptable for each application, services, and data.

Some of the things to consider when engineering your backup and disaster recovery blueprint include:

Availability of sufficient bandwidth and network capacity to redirect all users in case of a disaster.

Amount of data that may require backup.

Type of data to be protected

How long can it take to restore your systems from the cloud?

Communications Capacity enablement

Migrating to a cloud environment should make your business more agile and responsive to the market. Therefore, a robust communications enablement should be provided. Ideally, your cloud provider should be able to provide you with a contact center, unified messaging, mobility, presence, and integration with other business applications.

While the level of sophistication and efficiency of on-premise communications platforms depends on the capabilities of the company IT’s staff, cloud environments should offer communication tools with higher customizations to increase productivity.

A highly customized remote communications enablement will allow your company to refocus its IT resources to new innovation, spur agility, cut down on hardware costs and allow for more engagements with partners and customers.

Simply put, cloud communications:

Increase efficiency and productivity

Enables reimagined experience

Are designed for a seamless interaction.

legal liability and protection

Other important considerations when developing your cloud migration planning template are compliance with regulatory requirements and software licensing. For many businesses, data protection and regulatory compliance with HIPPA and GDPR is a constant concern, especially when dealing with identifiable data. Getting this right, the first time will allow you to move past the compliance issue blissfully.

When migrating, look for a cloud provider with comprehensive security assurance programs and governance-focused features. This will help your business operate more secure and in line with industry standards.

Ready to migrate your processes to a public cloud environment? Follow these pointers develop a comprehensive cloud migration planning template.

During the software engineering process, there are different issues which should be dealt with or else they will subject the project to unnecessary costs later. The technical debt perspective should be considered in each step of software development. For instance, when analyzing the cost of cloud approaches, you need to take into consideration the technical debt. You should as well factor the engineering aspect when making technical decisions such as choosing between cloud services vs. homegrown solutions.

What is technical debt?

Technical debt refers to the implied cost which will be incurred to do additional rework on a system after the engineering process is done. For example, engineers can choose to go for an easy option so that they can save time during the product design. The right steps which they will avoid will later need to be implemented which will mean a product has to be recalled or it will have to be fixed after it has reached the market which will cost more in terms of resources and manpower.

What are the most common types/causes technical debts?

Deliberate tech debt

In this case, engineers are aware of the step which is necessary during project implementation, but they will ignore it provided they can go for a shortcut which will save on cost and avail the product to the market. For instance, when analyzing the advantage of using the public cloud, some engineers may assume certain benefits, and later they will realize they are very necessary hence they are forced to go back and procure the system. It will lead to wastage in the company. Some engineers will not like doing the same process every now and then; they can avoid a given process only to expose the final product to flaws which will require re-engineering.

Accidental/outdated design tech debt

After designing a product or software, with time the technology will advance and render the design less effective in solving certain needs. For instance, due to advancement in technology, the tools you incorporated in a given software may end up being flawed which will make the product less effective which may necessitate re-engineering. Engineers may try their level best to come up with great designs, but advancement in technology can make their designs less effective.

Bit rot tech debt

It is a situation where a complexity develops over time. For example, a system or a component can develop unnecessary complexity due to different changes which have been incorporated over time. As engineers try to solve emerging needs, they can end up exposing the product to more complications which can be costly in the long run.

Strategies for minimizing technical debt

How to minimize deliberate tech debt

To avoid the tech debt, you need to track the backlog when engineers started the work. If you can track the backlog and identify areas where the engineers are trying to save time, you can avoid the debt.

Minimizing Accidental/outdated design tech debt

You need to refactor the subsystem every now and then so that you can identify the technical debt and fix it. For example, if the software is exposing you to unnecessary slowdowns, you need to fix the errors and make it meet industry standards.

Addressing Bit rot tech debt

Engineers should take time to understand the system they are running and clear any bad codes.

A public cloud strategy refers to a situation where you utilize cloud resources on a shared platform. Examples of shared or public cloud solutions include Microsoft Azure, Amazon Web Services and Google cloud. There are several benefits associated with cloud solutions. On the other hand, a private cloud strategy refers to a situation where you can decide to have an infrastructure which is dedicated to serving your business. It is sometimes referred to as homegrown where you employ experts to run the services so that your business can access different features. There are several advantages of using a public cloud over private cloud which you should know before you make an informed decision on the right platform to invest. Some of the benefits of the public cloud strategy include the following:

Availability and scale of Expertise

If you compare the public cloud and the private cloud services, the public cloud

allows you to access more experts. Remember the companies which offer the cloud services have enough employees who are ready to help several clients. In most cases, the other clients whom the service providers serve will not experience problems at the same time. It implies that human resource will be directed toward solving your urgent issue. You can as well scale up or down at any given time as the need arises which is unlike a case of private cloud solutions where you will have to invest in infrastructure each time you will like to upgrade.

Downgrading on a private cloud system can expose you to lose because you will leave some resources underutilized.

The volume of Technical Resources to apply

You access more technical resources in a public cloud platform. Remember the companies which offer the public cloud solutions are fully equipped with highly experienced experts. They also have the necessary tools and resources which

they can apply to assure you the best technical solutions each time you need them. It is unlike a private arrangement where you will have to incur more costs if the technical challenges will need advanced tools and highly qualified experts.

Price point

The price of a private cloud is high when compared to a public arrangement. If you are looking for ways you can save money, then the best way to go about it is to involve a public cloud solution. In the shared platform, you will only pay for

what you need. If you do not need a lot of resources at a given time, you can downgrade the services and enjoy fair prices. Services such as AWS offer great cost containment across the time which makes it easy to access the services at fair prices. For any business to grow, it should invest in the right package which brings the return on investment. The services offered by the public cloud systems allow businesses to save and grow. You should as well take into consideration other factors such as ecosystems for cloud relationships before you make an informed decision. There are some business models which prefer private cloud solutions while others can work well under public cloud-based solutions.

Unix and Linux are different operating systems with have some common commands. Source code for Linux is freely available to the public and Unix is not available. Linux operating system is a free/open source and Some versions of Unix are proprietary and others are a free/open source. Linux Operating system can be used for desktop systems and for servers. But the Unix is mainly used in servers, mainframes and high-end computers.

AIX is an operating system based on Unix versions from IBM. It is mainly designed for IBM’s workstations and for the server hardware platforms. And HP-UX is the operating system from HP ( Hewlett Packard ) based on Unix versions. HP-UX and AIX are stable operating system compare with Linux. HP-UX and AIX are platform dependent and they are limited to their own hardware. But in the case of Linux, it is platform independent and can be used with any hardware. Since HP-UX and AIX are platform dependent, they are optimised for the hardware and the performance is better than Linux operating systems. AIX is outperforming Linux from 5 to 10 percent.

Unix

AT&T Unix, started in the 1970s at the Bell Labs and newer versions of Unix have developed and some of them are listed below. In 1980, AT&T licensed Unix to third-party vendors and leading to the development of different variants. Some of them are;

Linux

Linux is a free and open source operating system based on Unix. Linux kernel was first developed by Linus Torvalds in 1991. Linux was originally developed for personal computers but nowadays it is using personal computers as well as in server systems. Since it is very flexible, it can be installed in any hardware systems. Linux operating system is available for mobile phones, tablets, video game consoles, mainframes and supercomputers. Some of the best distros for small business are;

Hardware architecture

Most commercial versions of UNIX distributions are coded for specific hardware. Like HP-UX for PA-RISC (Hewlett-Packard) and Itanium machines (Intel) and AIX is for Power processors ( IBM ). Since these distributions are limited, the developers can optimise their code for these architectures to get maximum utilisation of resources. Since it uses proprietary hardware, Unix distributions are not cost effective.

HP-UX needs HP or Intel hardware

AIX needs IBM Hardware

Linux operating system is not dependent on the hardware, so it can be installed in any of the server systems which have a processor. Since the developers cannot assume the hardware architecture and they need to prepare the code for some general hardware specifications and that’s why Linux operating system has less performance than the commercial Unix variants.

Linux is open to all hardware

Licensing

GNU General Public License (GPL), is a form of copyleft and is used for the Linux kernel and many of the components from the GNU Project. Free software projects, although developed through collaboration, are often produced independently of each other. AIX and HP-UX are using proprietary licenses.

HP-UX

Developer

Hewlett-Packard Enterprise

Written in

C

OS family

Unix (System V)

Initial release

1982; 36 years ago

Kernel type

Monolithic with dynamically loadable modules

License

Proprietary

IBM AIX

Developer

IBM

Written in

C

OS family

Unix

Initial release

1986; 32 years ago

Kernel type

Monolithic with dynamically loadable modules

License

Proprietary

Linux

Developer

Community, Linus Torvalds

Written in

Primarily C and assembly

OS family

Unix-like

Initial release

September 17, 1991; 26 years ago

Kernel type

Monolithic (Linux kernel)

License

GPLv2[7] and other free and open-source licenses (the name “Linux” is a trademark[b])

Softwares and Tools

Softwares and tools in Linux are general to all hardware. But in the case of Unix, separate tools and software which leverage to get the maximum performance. So the performance of the systems is higher than the Linux operating system by comparing the hardware configuration. Unix has good performance than Linux systems. While considering the cost estimation, Linux will get more votes.

System Management Interface Tool ( SMIT ) with AIX is the tools used for package management, System Administration Manager (SAM) on HP-UX. Linux operating system uses rpm or dpkg etc. based on the variants.

Software Installation and Patch Management

R H Linux

HP-UX

AIX

Install

rpm -i file

swinstall –s depot software

installp –a [-c] FileSet

Update

rpm -U/F file

swinstall –s depot software

installp –a FileSet

List

rpm -q

swlist –l product

lslpp –L all

Remove

rpm -e

swremove software

installp –u FileSet

Patches

rpm -u

swinstall

installp

List Patches

rpm -q -a

swlist –l product

lslpp –L all

Patch check

up2date/yum

security_patch_check

compare_report

File system

While talking about the file systems, Linux scores more than the other Unix versions. Unix supports two or three file systems locally. But Linux supports almost all the file systems available on any operating system.

System

Filesystem

AIX

jfs, gpfs

HP-UX

hfs, vxfs

Kernel

The kernel is the core of the operating system and the source code of the kernel are not freely available for the commercial versions of Unix. For the Linux operating system, the users can check and verify the code and even modify it if required.

Support

The commercial versions of Unix come with a license cost. Since these operating systems are purchased, the vendor will provide technical support to the end users to the smooth running of the operating systems.

In the case of the Linux operating system, we need to use the open source forums and community for getting support from the users and developers around the world or hire some freelancers for fixing the issues.

Over the years have occasionally use the action column feature, however, the last month or so I have found myself using it quite a lot. This is especially true in relation to the tea set and not just in relation to the change capture stage.

The first thing you need to know is, if you want to prevent getting the ‘no action column found’ notice on the target stage, need to ensure that the action column has been coded to be a single character field char (1). Otherwise, the Netezza connector stage will not recognize your field as an action column.

While most developers will commonly work with the action column feature in relation to the change capture stage, it can also be very useful if you have created a field from one or more inputs to tell you what behavior the row requires. I have found that this approach can be very useful and efficient under the right circumstances.

Example Pattern for Action Column Using Multiple Source Selects

Action column configuration example

Action Column Field Type

Change Code Values Mapping To Action Column

Here’s a quick reference table to provide the interpretation of the change type code to the actual one character action column value to which it will need to be interpreted.

Change Code Type

Change Type Code

Action Column Value

Copy (Data Without Changes)

0

No
value for this Change Type

Insert

1

I

Delete

2

D

Update

3

U

Example Transformer Stage, Derivation

Here is a quick transformer stage derivation coding example to take advantage of the action call capabilities. If you haven’t already handled the removal of the copy rows, you may also want to add a constraint.

The combination I most frequently find myself using is the insert and update combination.

I have found myself using this simple, but useful SQL time in recent weeks to research different issues and to help with impact analysis. So, I thought I would post it while I’m thinking about it. It just gives a list of views using a table, which can be handy to know. This SQL is simple and could be converted to an equi-join. I used the like statement mostly because I sometimes want to know if there are other views a similar nature in the same family (by naming convention) of tables.

Select All Fields From The _V_View

This is the simplest form of this SQL to views, which a table.

Select * from _v_view

where DEFINITION like ‘%<<TABLE_NAME>>%’ ;

Select Minimal Fields From The _V_View

This is the version of the SQL, which I normally use, to list the views, which use a table.

Cloud computing enables convenient, ubiquitous, measures, and on-demand access to a shared pool of scalable and configurable resources, such as servers, applications, databases, networks, and other services. Also, these resources can be provisioned and released rapidly with minimum interaction and management from the provider.

The rapidly expanding technology is rife with obscure acronyms, with major ones being SaaS, PaaS, and IaaS. These acronyms distinguish the three major cloud computing models discussed in this article. Notably, cloud computing virtually meets any imaginable IT needs in diverse ways. In effect, the cloud computing models are necessary to show the role that a cloud service provides and how the function is accomplished. The three main cloud computing paradigms can be demonstrated on the diagram shown below.

The three major cloud computing models

Infrastructure as a Service (IaaS)

In infrastructure as a service model, the cloud provider offers a service that allows users to process, store, share, and user other fundamental computing resources to run their software, which can include operating systems and applications. In this case, a consumer has minimum control over the underlying cloud infrastructure, but has significant control over operating systems, deployed applications, storage, and some networking components, such as the host firewalls.

Based on its description, IaaS can be regarded as the lowest-level cloud service paradigm, and possibly the most crucial one. With this paradigm, a cloud vendor provides pre-configured computing resources to consumers via a virtual interface. From the definition, IaaS pertains underlying cloud infrastructure but does not include applications or an operating system. Implementation of the applications, operating system, and some network components, such as the host firewalls is left up to the end user. In other words, the role of the cloud provider is to enable access to the computing infrastructure necessary to drive and support their operating systems and application solutions.

In some cases, the IaaS model can provide extra storage for data backups, network bandwidth, or it can provide access to enhanced performance computing which was traditionally available using supercomputers. IaaS services are typically provided to users through an API or a dashboard.

Features of IaaS

Users transfer the cost of purchasing IT infrastructure to a cloud provider

Infrastructure offered to a consumer can be increased or reduced depending on business storage and processing needs

The consumer will be saved from challenges and costs of maintaining hardware

High availability of data is in the cloud

Administrative tasks are virtualized

IaaS is highly flexible compared to other models

Highly scalable and available

Permits consumers to focus on their core business and transfer critical IT roles to a cloud provider

Infrastructure as a Service (IaaS)

IaaS Use Cases

A series of use cases can explore the above benefits and features afforded by IaaS. For instance, an organization that lacks the capital to own and manage their data centers can purchase an IaaS offering to achieve fast and affordable IT infrastructure for their business. Also, the IaaS can be expanded or terminated based on the consumer needs. Another set of companies that can deploy IaaS include traditional organizations seeking large computing power with low expenditure to run their workloads. IaaS model is also a good option for rapidly growing enterprises that avoid committing to specific hardware or software since their business needs are likely to evolve.

Popular IaaS Services

Major IT companies are offering popular IaaS services that are powering a significant portion of the Internet even without users realizing it.

Amazon EC2: Offers scalable and highly available computing capacity in the cloud. Allows users to develop and deploy applications rapidly without upfront investment in hardware

IBM’s SoftLayer: Cloud computing services offering a series of capabilities, such as computing, networking, security, storage, and so on, to enable faster and reliable application development. The solution features bare-metal, hypervisors, operating systems, database systems, and virtual servers for software developers.

ComputeNext: the solution empowers internal business groups and development teams with DevOps productivity from a single API.

Platform as a Service (PaaS)

Platform as a service model involves the provision of capabilities that allow users to create their applications using programming languages, tools, services, and libraries owned and distributed by a cloud provider. In this case, the consumer has minimum control over the underlying cloud computing resources such as servers, storage, and operating system. However, the user has significant control over the applications developed and deployed on the PaaS service.

In PaaS, cloud computing is used to provide a platform for consumers to deploy while developing, initializing, implementing, and managing their application. This offering includes a base operating system and a suite of development tools and solutions. PaaS effectively eliminates the needs for consumers to purchase, implement and maintain the computing resources traditionally needed to build useful applications. Some people use the term ‘middleware’ to refer to PaaS model since the offering comfortably sits between SaaS and IaaS.

Features of PaaS

PaaS service offers a platform for development, tasking, and hosting tools for consumer applications

PaaS is highly scalable and available

Offer cost effective and simple way to develop and deploy applications

Users can focus on developing quality applications without worrying about the underlying IT infrastructure

Software development companies and other enterprises that want to implement agile development methods can explore PaaS capabilities in their business models. Many PaaS services can be used in application development. PaaS development tools and services are always updated and made available via the Internet to offer a simple way for businesses to develop, test, and prototype their software solutions. Since developers’ productivity is enhanced by allowing remote workers to collaborate, PaaS consumers can rapidly release applications and get feedback for improvement. PaaS has led to the emergence of the API economy in application development.

Popular PaaS Offerings

There exist major PaaS services that are helping organizations to streamline application development. PaaS offering is delivered over the Internet and allows developers to focus more on creating quality and highly functional application while not worrying about the operating system, storage, and other infrastructure.

Google’s App Engine: the solution allows developers to build scalable mobile and web backends in any language in the cloud. Users can bring their own language runtimes, third-party libraries, and frameworks

IBM BlueMix: this PaaS solution from IBM allows developers to avoid vendor lock-in and leverage the flexible and open cloud environment using diverse IBM tools, open technologies, and third-party libraries and frameworks.

Heroku: the solution provides companies with a platform where they can build, deliver, manage, and scale their applications while abstracting and bypassing computing infrastructure hassles

Software as a Service (SaaS)

Software as a service model involves the capabilities provided to users by using a cloud vendor’s application hosted and running on a cloud infrastructure. Such applications are conveniently accessible from different platforms and devices through a web browser, a thin client interface, or a program interface. In this model, the end user has minimum control of the underlying cloud-based computing resources, such as servers, operating system, or the application capabilities

SaaS can be described as software licensing and delivery paradigm that features a complete and functional software solutions provided to users on a metered and subscription basis. Since users access the application via browsers or thin client and program interfaces, SaaS makes the host operating system insignificant in the operation of the product. As mentioned, the service is metered. In this case, SaaS customers are billed based on their consumption, while others pay a flat monthly fee.

On-premise hardware failure does not interfere with an application or cause data loss

Users can reduce or increase use of cloud-based resources depending on their processing and storage needs

Applications offered via SaaS model are accessible from any location and almost all Internet-enabled devices

Software as a Service (SaaS)

SaaS Use Cases

SaaS use case is a typical use case for many companies seeking to benefit from quality application usage without the need to develop, maintain and upgrade the required components. Companies can acquire SaaS solutions for ERP, mail, office applications, collaboration tool, among others. SaaS is also crucial for small companies and startups that wish to launch e-commerce service rapidly but lack the time and resource to develop and maintain the software or buy servers for hosting the platform. SaaS is also used by companies with short-term projects that require collaboration from different members located remotely.

Popular SaaS Services

SaaS offerings are more widespread as compared to IaaS and PaaS. In fact, a majority of consumers use SaaS services without realizing it.

Box: the SaaS offers secure file storage, sharing, and collaboration from any location and platform

Dropbox: modern application designed for collaboration and for creating, storing, and accessing files, docs, and folders.

Salesforce: the SaaS is among the leading customer relationship management platform that offers a series of capabilities for sales, marketing, service, and more.

Today, cloud computing models have revolutionized the way businesses deploy and manage computing resources and infrastructure. With the advent and evolution of the three major cloud computing models, that it IaaS, PaaS, and SaaS, consumers will find a suitable cloud offering that satisfies virtually all IT needs. These models’ capabilities coupled with competition from popular cloud computing service providers will continue availing IT solutions for consumers demanding for availability, enhanced performance, quality services, better coverage, and secure applications.

Consumers should review their business needs and do a cost-benefit analysis to approve the best model for their business. Also, consumers should conduct thorough workload assessment while migrating to a cloud service.

Globally, organizations are facing challenges emanating from data issues, including data consolidation, value, heterogeneity, and quality. At the same time, they have to deal with the aspect of Big Data. In other words, consolidating, organizing, and realizing the value of data in an organization has been a challenge over the years. To overcome these challenges, a series of strategies have been devised. For instance, organizations are actively leveraging on methods such as Data Warehouses, Data Marts, and Data Stores to meet their data assets requirements. Unfortunately, the time and resources required to deliver value using these legacy methods is a distressing issue. In most cases, typical Data Warehouses applied for business intelligence (BI) rely on batch processing to consolidate and present data assets. This traditional approach is affected by the latency of information.

Big Data

As the name suggests, Big Data describes a large volume of data that can either be structured or unstructured. It originates from business processes among other sources. Presently, artificial intelligence, mobile technology, social media, and the Internet of Things (IoT) have become new sources of vast amounts of data. In Big Data, the organization and consolidation matter more than the volume of the data. Ultimately, big data can be analyzed to generate insights that can be crucial in strategic decision making for a business.

Features of Big Data

The term Big Data is relatively new. However, the process of collecting and preserving vast amounts of information for different purposes has been there for decades. Big Data gained momentum recently with the three V’s features that include volume, velocity, and variety.

Volume: First, businesses gather information from a set of sources, such as social media, day-to-day operations, machine to machine data, weblogs, sensors, and so on. Traditionally, storing the data was a challenge. However, the requirement has been made possible by new technologies such as Hadoop.

Velocity: Another defining nature of Big Data is that it flows at an unprecedented rate that requires real-time processing. Organizations are gathering information from RFID tags, sensors, and other objects that need timely processing of data torrents.

Variety: In modern enterprises, information comes in different formats. For instance, a firm can gather numeric and structured data from traditional databases as well as unstructured emails, video, audio, business transactions, and texts.

Complexity: As mentioned above, Big Data comes from diverse sources and in varying formats. In effect, it becomes a challenge to consolidate, match, link, cleanse, or modify this data across an organizational system. Unfortunately, Big Data opportunities can only be explored when an organization successfully correlates relationships and connects multiple data sets to prevent it from spiraling out of control.

Variability: Big Data can have inconsistent flows within periodic peaks. For instance, in social media, a topic can be trending, which can tremendously increase collected data. Variability is also common while dealing with unstructured data.

Big Data Potential and Importance

The vast amount of data collected and preserved on a global scale will keep growing. This fact implies that there is more potential to generate crucial insights from this information. Unfortunately, due to various issues, only a small fraction of this data actually gets analyzed. There is a significant and untapped potential that businesses can explore to make proper and beneficial use of this information.

Analyzing Big Data allows businesses to make timely and effective decisions using raw data. In reality, organizations can gather data from diverse sources and process it to develop insights that can aid in reducing operational costs, production time, innovating new products, and making smarter decisions. Such benefits can be achieved when enterprises combine Big Data with analytic techniques, such as text analytics, predictive analytics, machine learning, natural language processing, data mining and so on.

Big Data Application Areas

Practically, Big Data can be used in nearly all industries. In the financial sector, a significant amount of data is gathered from diverse sources, which requires banks and insurance companies to innovate ways to manage Big Data. This industry aims at understanding and satisfying their customers while meeting regulatory compliance and preventing fraud. In effect, banks can exploit Big Data using advanced analytics to generate insights required to make smart decisions.

In the education sector, Big Data can be employed to make vital improvements on school systems, quality of education and curriculums. For instance, Big Data can be analyzed to assess students’ progress and to design support systems for professors and tutors.

Healthcare providers, on the other hand, collect patients’ records and design various treatment plans. In the healthcare sector, practitioners and service providers are required to offer accurate and timely treatment that is transparent to meet the stringent regulations in the industry and to enhance the quality of life. In this case, Big Data can be managed to uncover insights that can be used to improve the quality of service.

Governments and different authorities can apply analytics to Big Data to create the understanding required to manage social utilities and to develop solutions necessary to solve common problems, such as city congestion, crime, and drug use. However, governments must also consider other issues such as privacy and confidentiality while dealing with Big Data.

In manufacturing and processing, Big Data offers insights that stakeholders can use to efficiently use raw materials to output quality products. Manufacturers can perform analytics on big data to generate ideas that can be used to increase market share, enhance safety, minimize wastage, and solve other challenges faster.

In the retail sector, companies rely heavily on customer loyalty to maintain market share in a highly competitive market. In this case, managing big data can help retailers to understand the best methods to utilize in marketing their products to existing and potential consumers, and also to sustain relationships.

Challenges Handling Big Data

With the introduction of Big Data, the challenge of consolidating and creating value on data assets becomes magnified. Today, organizations are expected to handle increased data velocity, variety, and volume. It is now a business necessity to deal with traditional enterprise data and Big Data. Traditional relational databases are suitable for storing, processing, and managing low-latency data. Big Data has increased volume, variety, and velocity, making it difficult for legacy database systems to efficiently handle it.

Failing to act on this challenge implies that enterprises cannot tap the opportunities presented by data generated from diverse sources, such as machine sensors, weblogs, social media, and so on. On the contrary, organizations that will explore Big Data capabilities amidst its challenges will remain competitive. It is necessary for businesses to integrate diverse systems with Big Data platforms in a meaningful manner, as heterogeneity of data environments continue to increase.

Virtualization

Virtualization involves turning physical computing resources, such as databases and servers into multiple systems. The concept consists of making the function of an IT resource simulated in software, making it identical to the corresponding physical object. Virtualization technique uses abstraction to create a software application to appear and operate like hardware to provide a series of benefits ranging from flexibility, scalability, performance, and reliability.

Benefits of Virtualization

Achieving the economics of wide-scale functional virtualization using available technologies is easy to improve reliability by employing virtualization offered by cloud service providers on fully redundant and standby basis. Traditionally, organizations would deploy several services to operate at a fraction of their capacity to meet increased processing and storage demands. These requirements resulted in increased operating costs and inefficiencies. With the introduction of virtualization, the software can be used to simulate functionalities of hardware. In effect, businesses can outstandingly eliminate the possibility of system failures. At the same time, the technology significantly reduces capital expense components of IT budgets. In future, more resources will be spent on operating, than acquisition expenses. Company funds will be channeled to service providers instead of purchasing expensive equipment and hiring local personnel.

Overall, virtualization enables IT functions across business divisions and industries to be performed more efficiently, flexibly, inexpensively, and productively. The technology meaningfully eliminates expensive traditional implementations.

Apart from reducing capital and operating costs for organizations, virtualization minimizes and eliminates downtime. It also increases IT productivity, responsiveness, and agility. The technology provides faster provisioning of resources and applications. In case of incidents, virtualization allows fast disaster recovery that maintains business continuity.

Types of Virtualization

There are various types of virtualization, such as a server, network, and desktop virtualization.

In server virtualization, more than one operating system runs on a single physical server to increase IT efficiency, reduce costs, achieve timely workload deployment, improve availability and enhance performance.

Network virtualization involves reproducing a physical network to allow applications to run on a virtual system. This type of virtualization provides operational benefits and hardware independence.

In desktop virtualization, desktops and applications are virtualized and delivered to different divisions and branches in a company. Desktop virtualization supports outsourced, offshore, and mobile workers who can access simulate desktop on tablets and iPads.

Characteristics of Virtualization

Some of the features of virtualization that support the efficiency and performance of the technology include:

Partitioning: In virtualization, several applications, database systems, and operating systems are supported by a single physical system since the technology allows partitioning of limited IT resources.

Isolation: Virtual machines can be isolated from the physical systems hosting them. In effect, if a single virtual instance breaks down, the other machine, as well as the host hardware components, will not be affected.

Encapsulation: A virtual machine can be presented as a single file while abstracting other features. This makes it possible for users to identify the VM based on a role it plays.

Data Virtualization – A Solution for Big Data Challenges

Virtualization can be viewed as a strategy that helps derive information value when needed. The technology can be used to add a level of efficiency that makes big data applications a reality. To enjoy the benefits of big data, organizations need to abstract data from different reinforcements. In other words, virtualization can be deployed to provide partitioning, encapsulation, and isolation that abstracts the complexities of Big Data stores to make it easy to integrate data from multiple stores with other data from systems used in an enterprise.

Virtualization enables ease of access to Big Data. The two technologies can be combined and configured using the software. As a result, the approach makes it possible to present an extensive collection of disassociated and structured and unstructured data ranging from application and weblogs, operating system configuration, network flows, security events, to storage metrics.

Virtualization improves storage and analysis capabilities on Big Data. As mentioned earlier, the current traditional relational databases are incapable of addressing growing needs inherent to Big Data. Today, there is an increase in special purpose applications for processing varied and unstructured big data. The tools can be used to extract value from Big Data efficiently while minimizing unnecessary data replication. Virtualization tools also make it possible for enterprises to access numerous data sources by integrating them with legacy relational data centers, data warehouses, and other files that can be used in business intelligence. Ultimately, companies can deploy virtualization to achieve a reliable way to handle complexity, volume, and heterogeneity of information collected from diverse sources. The integrated solutions will also meet other business needs for near-real-time information processing and agility.

In conclusion, it is evident that the value of Big Data comes from processing information gathered from diverse sources in an enterprise. Virtualizing big data offers numerous benefits that cannot be realized while using physical infrastructure and traditional database systems. It provides simplification of Big Data infrastructure that reduces operational costs and time to results. Shortly, Big Data use cares will shift from theoretical possibilities to multiple use patterns that feature powerful analytics and affordable archival of vast datasets. Virtualization will be crucial in exploiting Big Data presented as abstracted data services.

Occasionally, one runs into the problem of hidden field values breaking join criteria. I have had to clean up bad archive and conversion data with hidden characters serval times over the last couple of weeks, so, I thought I might as well capture this note for future use.

I tried the Replace command which is prevalent for Netezza answers to this issue on the web, but my client’s version does not support that command. So, I needed to use the Translate command instead to accomplish it. It took a couple of searches of the usual bad actors to find the character causing the issue, which on this day was chr(0). Here is a quick mockup of the command I used to solve this issue.

Today, a business heavily depends on data to gain insights into their processes and operations and to develop new ways to increase market share and profits. In most cases, data required to generate the insights are sourced and located in diverse places, which requires reliable access mechanism. Currently, data warehousing and data virtualization are two principal techniques used to store and access the sources of critical data in a company. Each approach offers various capabilities and can be deployed for particular use cases as described in this article.

Data Warehousing

A data warehouse is designed and developed to secure host historical data from different sources. In effect, this technique protects data sources from performance degradation caused by the impact of sophisticated analytics and enormous demands for reports. Today, various tools and platforms have been developed for data warehouse automation in companies. They can be deployed to quicken development, automate testing, maintenance, and other steps involved in data warehousing. In a data warehouse, data is stored as a series of snapshots, where a record represents data at a particular time. In effect, companies can analyze data warehouse snapshots to compare data between different periods. The results are converted into insights required to make crucial business decisions.

Moreover, a data warehouse is optimized for other functions, such as data retrieval. The technology duplicates data to allow database de-normalization that enhances query performance. The solution is further deployed to create an enterprise data warehouse (EDW) used to service the entire organization.

Data Warehouse Information Architecture

Features of a Data Warehouse

A data warehouse is subject-oriented, and it is designed to help entities analyze data. For instance, a company can start a data warehouse focused on sales to learn more about sales data. Analytics on this warehouse can help establish insights such as the best customer for the period. The data warehouse is subject oriented since it can be defined based on a subject matter.

A data warehouse is integrated. Data from various sources is first out into a consistent format. The process requires the firm to resolve some challenges, such as naming conflicts and inconsistencies on units of measure.

A data warehouse in nonvolatile. In effect, data entered into the warehouse should not change after it is stored. This feature increases accuracy and integrity in data warehousing.

A data warehouse is time variant since it focuses on data changes over time. Data warehousing discovers trends in business by using large amounts of historical data. In effect, a typical operation in a data warehouse scans millions of rows to return an output.

A data warehouse is designed and developed to handle ad hoc queries. In most cases, organizations may not predict the amount of workload of a data warehouse. Therefore, it is recommendable to optimize the data warehouse to perform optimally over any possible query operation.

Advantages of Data Warehousing

The primary motivation for developing a data warehouse is to provide timely information required for decision making in an organization. A business intelligence data warehouse serves as an initial checkpoint for crucial business data. When a company stores its data in a data warehouse, tracking it becomes natural. The technology allows users to perform quick searches to be able to retrieve and analyze static data.

Another driver for companies investing in data warehouses involves integrating data from disparate sources. This capability adds value to operational applications like customer relationship management systems. A well-integrated warehouse allows the solution to translate information to a more usable and straightforward format, making it easy for users to understand the business data.

The technology also allows organizations to perform a series of analysis on data.

A data warehouse reduces the cost to access historical data in an organization.

Data warehousing provides standardization of data across an organization. Moreover, it helps identify and eliminate errors. Before loading data, the solution shows inconsistencies to users and corrects them.

A data warehouse also improves the turnaround time for analysis and report generation.

The technology makes it easy for users to access and share data. A user can conduct a quick search on a data warehouse to find and analyze static data without wasting time.

Disadvantages of Data Warehousing

While data warehousing technology is undoubtedly beneficial to many organizations, not all data warehouses are relevant to a business. In some cases, a data warehouse can be expensive to scale and maintain.

Preparing a data warehouse is time-consuming since it requires users to input raw data, which has to be achieved manually.

A data warehouse is not a perfect choice for handing unstructured and complex raw data. Moreover, it faces difficulties incompatibility. Depending on the data sources, companies may require a business intelligence team to ensure compatibility is achieved for data coming from sources running distinct operating systems and programs.

The technology requires a maintenance cost to continue working correctly. The solution needs to be updated with latest features that might be costly. Regularly maintaining a data warehouse will need a business to spend more on top of the initial investment.

A data warehouse use can be limited due to information privacy and confidentiality issues. In most cases, businesses collect and store sensitive data belonging to their clients. Viewing it is only allowed to individual employees, which limits the benefits offered by a data warehouse.

Data Warehousing Use Case

There are a series of ways organizations use data warehouses. Businesses can optimize the technology for performance by identifying the type of data warehouse they have.

A data warehouses can be used by an organization that is struggling to report efficiently on business operations and activities. The solution makes it possible to access the required data

A data warehouse is necessary for an organization where data is copied separately by different divisions for analysis in spreadsheets that are not consistent with one another.

Data warehousing is crucial in organizations where uncertainties about data accuracy are causing executives to question the veracity of reports.

A data warehouse is crucial for business intelligence acceleration. The technology delivers rapid data insights to analysts at different scales, concurrency, and without requiring manual tuning or optimization of a database.

Data Virtualization Information Architecture

Data Virtualization

Data virtualization technology does not require transfer or storage of data. Instead, users employ a combination of application programming interfaces (APIs) and metadata (data about data) to interface with data in different sources. Users use joined queries to gain access to the original data sources. In other words, data virtualization offers a simplified and integrated view to business data in real-time as requested by business users, applications, and analytics. In effect, the technology makes it possible to integrate data from distinct sources, formats, and locations, without replication. It creates a unified virtual data layer that delivers data services to support users and various business applications.

Data virtualization performs many of the same data integration functions, that is, extract, transform, and load, data replication, and federation. It leverages modern technology to deliver real-time data integration with agility, low cost, and high speed. In effect, data virtualization eliminates traditional data integration and reduces the need for replicated data warehouses and data marts in most cases.

Capabilities and Benefits of Data Virtualization

There are various benefits of implementing data virtualization in an organization.

Firstly, data virtualization allows access and leverage of all information that helps a firm achieve a competitive advantage. The solution offers a unified virtual layer that abstracts the underlying source complexity and presents disparate data sources as a single source.

Data virtualization is cheaper since it does not require actual hardware devices to be installed. In other words, organizations no longer need to purchase and dedicate a lot of IT resources and additional monetary investment to create on-site resources, similar to the one used in a data warehouse.

Data virtualization allows speedy deployment of resources. In this solution, resource provisioning is fast and straightforward. Organizations are not required to set up physical machines or to create local networks or install other IT components. Users have a single point of access to a virtual environment that can be distributed to the entire company.

Data virtualization is an energy-efficient system since the solution does not require additional local hardware and software. Therefore, an organization will not be required to install cooling systems.

Disadvantages of Data Virtualization

Data virtualization creates a security risk. In the modern world, having information is a cheap way to make money. In effect, company data is frequently targeted by hackers. Implementing data virtualization from disparate sources may give an opportunity to malicious users to steal critical information and use it for monetary gain.

Data virtualization requires a series of channels or links that must work in cohesion to perform the intended task. In this cases, all data sources should be available for virtualization to work effectively.

Data Virtualization Use Cases

Companies that rely on business intelligence require data virtualization for rapid prototyping to meet immediate business needs. Data virtualization can create a real-time reporting solution that unifies access to multiple internal databases.

Provisioning data services for single-view applications, such as in customer service and call center applications require data virtualization.

End of Support for IBM InfoSphere Information Server 9.1.0

IBM InfoSphere Information Server 9.1.0 will reach End of Support on 2018-09-30. If you are still on the InfoSphere Information Server (IIS) 9.1.0, I hope you have a plan to migrate to an 11-series version soon. InfoSphere Information Server (IIS) 11.7 would be worth considering if you don’t already own an 11-series license. InfoSphere Information Server (IIS) 11.7 will allow you to take advantage of the evolving thin client tools and other capabilities in the 2018 release pipeline without needing to perform another upgrade.

Related References

IBM Support, End of support notification: InfoSphere Information Server 9.1.0

Machine learning is Artificial Intelligence (AI) which enables a system to learn from data rather than through explicit programming. Machine learning uses algorithms that iteratively learn from data to improve, describe data, and predict outcomes. As the algorithms ingest training data to produce a more precise machine learning model. Once trained, the machine learning model, when provided data will generate predictions based on the data that taught the model. Machine learning is a crucial ingredient for creating modern analytics models.

I had a reason this week to perform a substring on a character in Netezza this week, something I have not had a need to do before. The process was not as straightforward as I would have thought, since the command is explained as a static position command, and the IBM documentation, honestly, wasn’t much help. Knowing full well, that text strings are variable having to provide a static position is not terribly useful in and of itself. So, we need to use an expression to make the substring command flexible and dynamic.

I did get it work the way I needed, but it took two commands to make it happen:

The First was the ’instr’ command to identify the field and character I wanted to substring on: instr(<<FIELD_NAME>>,’~’) as This provides the position number of the tilde (~).

The second was the ‘substr’ command in which I embedded the ‘instr’ command: substr(<<FIELD_NAME>>,0,instr(<<FIELD_NAME>>,’~’) )

This worked nicely for what I needed, which was to pick out a file name from the beginning of a string, which was delimited with a tilde (~)

Substring on a Character Command Format

This format example starts with position zero (0) as position 1 of substring command and goes to the first tilde (~) as position 2 of the substring command.

During the course of the week, the discussion happened regarding the different places where a person might read the DataStage and QualityStage logs in InfoSphere. I hadn’t really thought about it, but here are a few places that come to mind:

While investigating a recent Infosphere Information Server (IIS), Datastage, Essbase Connect error I found the explanations of the probable causes of the error not to be terribly meaningful. So, now that I have run our error to ground, I thought it might be nice to jot down a quick note of the potential cause of the ‘Client Commands are Currently Not Being Accepted’ error, which I gleaned from the process.

Error Message Id

IIS-CONN-ESSBASE-01010

Error Message

An error occurred while processing the request on the server. The error information is 1051544 (message on contacting or from application:[<<DateTimeStamp>>]Local////3544/Error(1013204) Client Commands are Currently Not Being Accepted.

Possible Causes of The Error

This Error is a problem with access to the Essbase object or accessing the security within the Essbase Object. This can be a result of multiple issues, such as:

I encountered, what I will admit is a pet peeve today, which is why I’m writing this article. I needed contact someone whom I correspond with regularly, but I have no reason to call or be called by them. So, after checking my phone, went to their email thinking this would be a fast and easy way to gather the contact information. Well, not true. I did eventually gather the information and contact the person, but what a waste of time, which is time they are being billed for one way or another.

Example Signature Block

Which email should have a signature block?

The signature block should be on every email (both initiated by you and replied to by you), this was true even before the days of remote work, but for remote workers, contingent works, and works who travel frequently it can be a productive enhancer.

Plus, it is simply the professional thing to do and saves everyone time and frustration. Not to mention it makes you look unprofessional not having one. do you really want to do that to your personal brand?

As if that were not enough, including your signature block is free advertising for you and the company you represent.

Additionally, most email accounts let you build one or more signature block, which can be embedded in your email.

Where to place your Signature Block?

The signature block should go at the bottom of your email. I still use the five lines below the last line of the body of the email to provide white space before the closing, as I learned when writing business letters decades ago.

What should be in a signature Block?

The signature block should be compact and informative and at a minimum should include:

The Closing

The closing is simply a polite way of saying I’m ending my message now. I usually go with the tried and true ‘Sincerely’, but others go with ‘Thank you’, ‘Best Regards’, or ‘Best Wishes,’. The main points, it should be short, polite, and professional.

This section should be followed by two lines

Your Name

This line is your professional name (First Name, Middle Initial, and Last name) and designations (Ph.D.…etc.)

This is your chance to say who you are and brand yourself to the reader, in a way which your email address cannot. Especially, when you consider that many of us don’t control what work email address is assigned to us.

Your Business Title

Including your business title provides some insight into your role and professional expertise.

Your Company Name

Much like your title, providing the Company Name and Address lets the reader know who you represent and, perhaps, more importantly, it is free advertising for the company.

Your Phone Numbers

Including your phone numbers, both office and cell (if different) enable people to quickly reach out to you if they need or want to. Not everybody keeps all their infrequent business contacts in the phone directory.

Putting your phone numbers on your signature block, also, enable the potential caller to verify that the numbers which they may have are still correct.

There are other items are sometimes included, such as:

A company logo to enhance the appearance and quality of a signature block

The Company’s website to help customer find out more about the company and to direct business to the company

The senders email to reinforce the email address in the header of the email.

However, the guidance provided above will make you look a lot more professional in a hurry if you have not been including a signature block in your emails.